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Basics of Machine Learning (ML) and TensorFlow

In this course we will learn the different aspects of Machine Learning (ML). We will begin by looking into the different problems that we can solve with ML and when and why we must use ML. We will put this knowledge into practice by building our own model. At the end of this course, we will be able to classify images based on the digit shown with 99% accuracy.

We will learn how to train a model, how to check the ability of a model to predict the good prediction, how to avoid over-learning and how to change the parameters to get a better result. We will look at different techniques that can be used to solve different cases of problems, and we will learn the strengths and weaknesses of each technique.

To apply machine learning in practice we will learn to use TensorFlow, an extension in Python that will allow us to apply all the techniques we learned in the theoretical approach. We will learn the syntax of all the different steps of ML: how to train a model, how to validate the model, and how to use it to make predictions in practice.

Machine Learning will be the main engine to build the world of the future. This course will convince you of the possibilities that ML can offer. Once a company has collected enough data, ML can help automate tasks or predict future data.

After attending this course the participant

Schedule

Momenteel zijn er voor deze cursus geen publieke sessies gepland. Graag organiseren we een bedrijfssessie voor u of een extra publieke sessie (bij voldoende belangstelling). Geïnteresseerd? Gelieve dan ABIS te contacteren.

Intended for

This course is for anyone who has (almost) never practised Machine Learning and who does not know Machine Learning theory.

Background

Good Python programming knowledge is a prerequisite (see Python fundamentals).

Main topics

Training method

Classroom teaching, focused on practical examples and supported by in-depth exercises and individual practice.

Duration

7 days.

Course leader

ML6.


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